`— title: “” output: html_document: df_print: paged —

Import data

  d = read.csv('hc.csv')
  cat("Total patients: ", nrow(d))

## Total patients:  1160

Compute cohort stats

get_range = function(d, na.rm=F, version="memima"){
  if (version=="mimame"){
    (paste("(",min(d, na.rm=na.rm), "-", max(d, na.rm=na.rm),",",round(mean(d, na.rm=na.rm),2),")", sep=""))
  } else if (version=="memima") {
    paste( round(mean(d, na.rm=na.rm),2), " (", min(d, na.rm=na.rm), "-", max(d, na.rm=na.rm), ")", sep="")
  } else if (version=="iqr") {
    paste( 
      round(median(d, na.rm=na.rm),2), 
      " (",
      paste(quantile(d, na.rm=T, probs=c(0.25,0.75), names=F), collapse="-"),
      ")",
      sep="")
  } else if (version=="iqrp") {
    paste( 
      round(mean(d, na.rm=na.rm),2),
      "/",
      round(median(d, na.rm=na.rm),2), 
      " (",
      paste(quantile(d, na.rm=T, probs=c(0.25,0.75), names=F), collapse="-"),
      ")",
    sep="")
  }
}

print_stats = function(d){
  cat("Age-Median(IQR), in years :", get_range(d$Edad, version="iqr"),"\n")
  cat("Sex male n(%):", 
      sum(d$Varones=="1", na.rm=TRUE),
      "(",round(mean(d$Varones, na.rm=TRUE)*100,2),"%)\n")
  cat("DeltaDDXCovSimptoms", get_range(d$DeltaDDXCOVSimptomes, version="iqr"),"\n")
  cat("Ct", get_range(d$Ct, version="iqr"),"\n")
  cat("---\n")
  cat("Hypertension arterial:", round(mean(d$HTA, na.rm=TRUE)*100,2),"%\n")
  cat("Diabetes mellitus:", round(mean(d$Diabetes_mellitus, na.rm=TRUE)*100,2),"%\n")
  cat("Chronic heart disease:", round(mean(d$Cardiopatía_crónica, na.rm=TRUE)*100,2),"%\n")
  cat("Chronic lung disease:", round(mean(d$Enfermedad_respiratoria_cronica, na.rm=TRUE)*100,2),"%\n")
  cat("Hematological malignances:", round(mean(d$Enfermedad_hematológica, na.rm=TRUE)*100,2),"%\n")
  cat("Chronic liver diseases:", round(mean(d$Hepatopatía_crónica, na.rm=TRUE)*100,2),"%\n")
  cat("Solid neoplasm:", round(mean(d$NEO_SOLIDA_MALIGNA, na.rm=TRUE)*100,2),"%\n")
  
  cat("---\n")
  cat("Temperatura:", get_range(d$Temp_d0, na.rm=T, version="iqr"),"\n")
  cat("Respiratory rate:", get_range(d$FR_d0, na.rm=T, version="iqr"),"\n")
  cat("Oxygen saturation:", get_range(d$SatO2_d0, na.rm=T, version="iqr"),"\n")
  
  cat("---\n")
  cat("Ferritin:", get_range(d$Ferritina_ing, na.rm=T, version="iqr"),"\n")
  cat("C-RP:", get_range(d$PCR_ing, na.rm=T, version="iqr"),"\n")
  cat("D-dimer:", get_range(d$Dimerod_ing, na.rm=T, version="iqr"),"\n")
  cat("LDH:", get_range(d$LDH_ing, na.rm=T, version="iqr"),"\n")
  cat("Lymphocyte count:", get_range(d$Linfocitos_ing, na.rm=F, version="iqr"),"\n")
  
  cat("---\n")
  cat("Ct at Covid19 ddx:", get_range(d$Ct, na.rm=F, version="iqr"), "\n")
  cat("<=20:", round(mean(d$Ct<=20,)*100,2),"%\n")
  cat("21-25:", round(mean(d$Ct>20 & d$Ct<25,)*100,2),"%\n")
  cat("<=20:", round(mean(d$Ct>=25,)*100,2),"%\n")
  
  cat("---\n")
  cat("DDXCovSimptomes", get_range(d$DeltaDDXCOVSimptomes, version="iqr"), "\n")
  cat("UCI admission:", round(mean(d$UCI=="1",)*100,2),"%\n")
  cat("60-days mortality:", round(mean(d$MORT_60d=="1",)*100,2),"%\n")
}

print_stats(d)

## Age-Median(IQR), in years : 66 (55-78) 
## Sex male n(%): 688 ( 59.41 %)
## DeltaDDXCovSimptoms 6 (3-8) 
## Ct 26.48 (22-30.57) 
## ---
## Hypertension arterial: 44.57 %
## Diabetes mellitus: 19.05 %
## Chronic heart disease: 25.09 %
## Chronic lung disease: 23.79 %
## Hematological malignances: 6.72 %
## Chronic liver diseases: 6.12 %
## Solid neoplasm: 15.26 %
## ---
## Temperatura: 37.3 (36.6-38) 
## Respiratory rate: 20 (18-24) 
## Oxygen saturation: 95 (93-97) 
## ---
## Ferritin: 580 (274-1088) 
## C-RP: 7.94 (3.765-14.17) 
## D-dimer: 700 (400-1300) 
## LDH: 315 (251-400) 
## Lymphocyte count: 0.8 (0.6-1.1) 
## ---
## Ct at Covid19 ddx: 26.48 (22-30.57) 
## <=20: 14.91 %
## 21-25: 25.6 %
## <=20: 59.48 %
## ---
## DDXCovSimptomes 6 (3-8) 
## UCI admission: 21.03 %
## 60-days mortality: 14.22 %

# Load cluster information (computed in Python code)
hc_clusters = read.csv('hc_w_cluster.csv')
d$cluster = hc_clusters$cluster

Graphs

library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

fvpal = c("#E07871","#AC991A","#1AAF47","#1AB3B7","#6897E6")# ,"#De6AD0")

fg3d = plot_ly(
             x=d$DeltaDDXCOVSimptomes, y=d$Ct, z=d$Linfocitos_ing,
             type="scatter3d",
             mode="markers",
             marker = list(symbol = 'circle', size = 2), 
             color=as.character(d$cluster),
             colors=fvpal
             ) %>%
  layout(
    scene=list(
      xaxis=list(title='pre-test duration of symptoms', range=list(0,20)),
      yaxis=list(title='Ct of PCR at diagnosis', range=list(0,35)),
      zaxis=list(title='Lymphocites', range=list(0,2.5)),
      colorway=fvpal
    ),
    legend=list(itemsizing='constant'),
    colorway=fvpal
  )

fg3d

PCA (Principal component Analysis)

library(factoextra)

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

dkmeans = select(d, Ct, DeltaDDXCOVSimptomes, Linfocitos_ing)
res.pca = prcomp(dkmeans, scale=TRUE)
res.var = get_pca_var(res.pca)

cluster_info = list(data = dkmeans, cluster = d$cluster)
fviz_cluster(cluster_info, data=dkmeans, geom=c("point"), axes = c(1,2), palette=fvpal)

Death patients blob

options(rgl.useNULL=TRUE) # Set to TRUE for knit
library(rgl)
library(ks)
library(misc3d)

gd = filter(d, MORT_60d=="1")
plot3d(x=gd$DeltaDDXCOVSimptomes, y=gd$Ct, z=gd$Linfocitos_ing, 
       xlab="", ylab="", zlab="",
       xlim=c(0,20), ylim=c(0,37), zlim=c(0,2.5))

gd.dens3d <- kde(x=gd[,c("DeltaDDXCOVSimptomes", "Ct", "Linfocitos_ing")], gridsize=c(64,64,64), compute.cont=T) # This will be added to the next one

gd.dens3d <- kde(x=gd[,c("DeltaDDXCOVSimptomes", "Ct", "Linfocitos_ing")], gridsize=c(64,64,64), compute.cont=T)

x.latt <- gd.dens3d$eval.points[[1]]
y.latt <- gd.dens3d$eval.points[[2]]
z.latt <- gd.dens3d$eval.points[[3]]

qcols <- c("red", "red4")
qalpha <- c(0.2, 0.75)
qlevels <- gd.dens3d$cont[c(75,25)] # quartiles

contour3d(x=x.latt,y=y.latt,z=z.latt,f=gd.dens3d$estimate,
           color=qcols, level=qlevels, add=TRUE, alpha=qalpha,
          xlim=c(0,20), ylim=c(0,37), zlim=c(0,2.5))

Dead patients per cluster plots

op=1
d$size = 2
d$type = d$MORT_60d + d$Remdesivir*2

d$color[d$type==0] = "rgba(126,0,0,1)"
d$color[d$type==1] = "rgba(255,0,0,1)"
d$color[d$type==2] = "rgba(0,150,0,1)"
d$color[d$type==3] = "rgba(0,255,0,1)"
d$size[d$type==3] = 4

d1 = filter(d, d$cluster==1)
fig1 = plot_ly(
             x=d1$DeltaDDXCOVSimptomes, y=d1$Ct, z=d1$Linfocitos_ing,
             type="scatter3d",
             mode="markers",
             marker = list(color=d1$color, symbol = 'circle', size = d1$size, opacity=op, line = list(width=0)), 
             scene='scene1'
             ) 

d2 = filter(d, d$cluster==2)
fig2 = plot_ly(
             x=d2$DeltaDDXCOVSimptomes, y=d2$Ct, z=d2$Linfocitos_ing,
             type="scatter3d",
             mode="markers",
             marker = list(color=d2$color, symbol = 'circle', size =d2$size, opacity=op, line = list(width=0)), 
             scene='scene2'
             ) 

d3 = filter(d, d$cluster==3)
fig3 = plot_ly(
             x=d3$DeltaDDXCOVSimptomes, y=d3$Ct, z=d3$Linfocitos_ing,
             type="scatter3d",
             mode="markers",
             marker = list(color=d3$color, symbol = 'circle', size = d3$size, opacity=op, line = list(width=0)), 
             scene = 'scene3'
             ) 

d4 = filter(d, d$cluster==4)
fig4 = plot_ly(
             x=d4$DeltaDDXCOVSimptomes, y=d4$Ct, z=d4$Linfocitos_ing,
             type="scatter3d",
             mode="markers",
             marker = list(color=d4$color, symbol = 'circle', size = d4$size, opacity=op, line = list(width=0)), 
             scene = 'scene4'
             ) 

d5 = filter(d, d$cluster==5)
fig5 = plot_ly(
             x=d5$DeltaDDXCOVSimptomes, y=d5$Ct, z=d5$Linfocitos_ing,
             type="scatter3d",
             mode="markers",
             marker = list(color=d5$color, symbol = 'circle', size = d5$size, opacity=op, line = list(width=0)), 
             scene = 'scene5'
             ) 


cl = 100
axx <- list(
  title='d',
  gridcolor='rgb(cl, cl, cl)',
  range=c(0,20)
)
axy <- list(
  title='Ct',
  gridcolor='rgb(cl, cl, cl)',
  zerolinecolor='rgb(0, 0, 0)',
  range=c(0,37)
)
axz <- list(
  title='L',
  gridcolor='rgb(cl, cl, cl)',
  zerolinecolor='rgb(0, 0, 0)',
  range=c(0,2.5)
)

figg = subplot(fig1, fig2, fig3, fig4, fig5, fig5)
figg <- figg %>% layout(title = "3D Subplots", autosize=F,
                        

scene = list(domain=list(x=c(0,0.33),y=c(0.5,1)),
              xaxis=axx, yaxis=axy, zaxis=axz,
              aspectmode='cube'),

  scene2 = list(domain=list(x=c(0.33,0.66),y=c(0.5,1)),
               xaxis=axx, yaxis=axy, zaxis=axz,
               aspectmode='cube'),
  
  scene3 = list(domain=list(x=c(0.66,1),y=c(0.5,1)),
               xaxis=axx, yaxis=axy, zaxis=axz,
               aspectmode='cube'),
  
  scene4 = list(domain=list(x=c(0,0.33),y=c(0,0.5)),
               xaxis=axx, yaxis=axy, zaxis=axz,
               aspectmode='cube'),
  
  scene5 = list(domain=list(x=c(0.33,0.66),y=c(0,0.5)),
               xaxis=axx, yaxis=axy, zaxis=axz,
               aspectmode='cube'),
  
  scene6 = list(domain=list(x=c(0.66,1),y=c(0,0.5)),
               xaxis=axx, yaxis=axy, zaxis=axz,
               aspectmode='cube')
)

figg = figg %>%
  style(hoverinfo = 'none', showlegend=F)

## Warning: 'layout' objects don't have these attributes: 'NA'
## Valid attributes include:
## '_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'

# This will look good in Knit
  layout(figg, height=800, width=1200)

## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()

## Warning: 'layout' objects don't have these attributes: 'NA'
## Valid attributes include:
## '_deprecated', 'activeshape', 'annotations', 'autosize', 'autotypenumbers', 'calendar', 'clickmode', 'coloraxis', 'colorscale', 'colorway', 'computed', 'datarevision', 'dragmode', 'editrevision', 'editType', 'font', 'geo', 'grid', 'height', 'hidesources', 'hoverdistance', 'hoverlabel', 'hovermode', 'images', 'legend', 'mapbox', 'margin', 'meta', 'metasrc', 'modebar', 'newshape', 'paper_bgcolor', 'plot_bgcolor', 'polar', 'scene', 'selectdirection', 'selectionrevision', 'separators', 'shapes', 'showlegend', 'sliders', 'spikedistance', 'template', 'ternary', 'title', 'transition', 'uirevision', 'uniformtext', 'updatemenus', 'width', 'xaxis', 'yaxis', 'barmode', 'bargap', 'mapType'

Legend

Legend for the previous plot

plot.new()
legend("right", c("No Remdesivir - Survived", "No Remdesivir - Dead (60-days)", "Remdesivir - Survived", "Remdesivir - Dead (60-days)"),
       pch=19,
       col=c(rgb(126/255,0,0), rgb(255/255,0,0), rgb(0,150/255,0), rgb(0,255/255,0)))